README.md

metadata

title: DynaMix
emoji: 🌀
colorFrom: red
colorTo: blue
sdk: gradio
sdk_version: 5.43.1
app_file: app.py
pinned: false
license: cc-by-4.0
short_description: Zero-shot forecasting of Dynamical Systems using DynaMix

DynaMix: Zero-shot Forecasting of Dynamical Systems

This DynaMix demo is an interactive Gradio app for zero-shot dynamical systems reconstruction using the DynaMix architecture (accepted NeurIPS 2025 paper ). It loads pretrained models from the Hugging Face Hub (see DynaMix model) and provides predictions from uploaded context data.

Key Features

• Zero-shot forecasting: Powered by DynaMix model architecture
• Custom Context Upload: Upload your CSV/NPY data or choose a preset (Lorenz63, Noisy Sine, Chua, Selkov)
• Interactive Settings: Configure forecast settings
• Visualizations: Plots of context data and forecast
• Exports: Download forecast as CSV and NPY

Using the Application

Data Input

You can either upload your own data or choose a preset dataset from the left panel.

• Upload: Accepts .csv or .npy files
• Presets: Noisy Sine, Lorenz63, Chua, Selkov

Supported data formats:

• NPY files: Numpy array of shape (time_steps, dimensions). 1D time series arrays are auto-expanded to (time_steps, 1); otherwise must be 2D with at least 2 time steps and ≥1 dimension.

• CSV files: Each column is a dimension; each row is a time step. Only numeric columns are used. Data must be 2D with at least 2 time steps and ≥1 dimension.

Example CSV format:

dim_1,dim_2,dim_3
0.1,0.2,0.3
0.4,0.5,0.6
0.7,0.8,0.9

Forecast Settings

• Model Selection: Select the pretrained model to use for forecasting.

• Forecast Length: Number of future steps to generate (1–2001, step 100, default 512)

• Advanced Settings

  • Preprocessing Method: Method to use for preprocessing the context data (for cases where input dims < model dims)
  • Standardize: Normalize context to zero mean and unit variance (default: enabled)
  • Fit Nonstationary: Account for non-stationary trends in the data (default: disabled)
  • Context Steps: Maximum number of last steps from the uploaded data to use as context. If your uploaded sequence is longer, it will be truncated to the most recent Context Steps. (default 2048)

Outputs

• Interactive Plot: Shows historical context (blue) and forecast (red) per dimension, up to 15 dimensions.
• Files:
  • forecast.csv: Full forecast for all dimensions.
  • forecast.npy: Full forecast ndarray including all dimensions.

License

This project is released under the CC BY 4.0 license.